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import json
import os
import torch
from diffusers import UNet1DModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def unet(hor):
if hor == 128:
down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
block_out_channels = (32, 128, 256)
up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D")
elif hor == 32:
down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
block_out_channels = (32, 64, 128, 256)
up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
model = torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
state_dict = model.state_dict()
config = dict(
down_block_types=down_block_types,
block_out_channels=block_out_channels,
up_block_types=up_block_types,
layers_per_block=1,
use_timestep_embedding=True,
out_block_type="OutConv1DBlock",
norm_num_groups=8,
downsample_each_block=False,
in_channels=14,
out_channels=14,
extra_in_channels=0,
time_embedding_type="positional",
flip_sin_to_cos=False,
freq_shift=1,
sample_size=65536,
mid_block_type="MidResTemporalBlock1D",
act_fn="mish",
)
hf_value_function = UNet1DModel(**config)
print(f"length of state dict: {len(state_dict.keys())}")
print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
mapping = dict((k, hfk) for k, hfk in zip(model.state_dict().keys(), hf_value_function.state_dict().keys()))
for k, v in mapping.items():
state_dict[v] = state_dict.pop(k)
hf_value_function.load_state_dict(state_dict)
torch.save(hf_value_function.state_dict(), f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin")
with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json", "w") as f:
json.dump(config, f)
def value_function():
config = dict(
in_channels=14,
down_block_types=("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
up_block_types=(),
out_block_type="ValueFunction",
mid_block_type="ValueFunctionMidBlock1D",
block_out_channels=(32, 64, 128, 256),
layers_per_block=1,
downsample_each_block=True,
sample_size=65536,
out_channels=14,
extra_in_channels=0,
time_embedding_type="positional",
use_timestep_embedding=True,
flip_sin_to_cos=False,
freq_shift=1,
norm_num_groups=8,
act_fn="mish",
)
model = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
state_dict = model
hf_value_function = UNet1DModel(**config)
print(f"length of state dict: {len(state_dict.keys())}")
print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
mapping = dict((k, hfk) for k, hfk in zip(state_dict.keys(), hf_value_function.state_dict().keys()))
for k, v in mapping.items():
state_dict[v] = state_dict.pop(k)
hf_value_function.load_state_dict(state_dict)
torch.save(hf_value_function.state_dict(), "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin")
with open("hub/hopper-medium-v2/value_function/config.json", "w") as f:
json.dump(config, f)
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
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